Activity Pattern Mining from Social Media for Healthcare Monitoring on Big data
N. Priya1, S. Sangeetha2, S. Amudha3
1N.Priya , Department of CSE, Bharath Institute of Higher Education and Research, Chennai, India.
2Ms. S. Sangeetha, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, India.
3Ms. S. Amudha, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
Manuscript received on 04 July 2019 | Revised Manuscript received on 17 July 2019 | Manuscript Published on 23 August 2019 | PP: 527-530 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I31020789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3102.0789S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Big data applications introduce novel openings for establishinginnovative information and produce differentadvanced methods to improve the worth of healthcare.In this paper, a novel activity pattern mining from social media for healthcare to examine big data applications in different biomedical multi-disciplines such as bioinformatics, medical imaging and community healthcare applications.Big data analytical tools perform the key part in their task for extracting hidden behavioural and expressive patterns frompersonal messages and their tweets. The behavioural patterns of the users can realizetheir additional informations about their concealed feelings and sentiments[1],[ 3],[5]. Further, the neural network is modelled to predict the psychological informations, such as nervousness, depression, behavioural disorder and mental stress.This is also shows that integrating variety of sources of data enables medical practitioner to show a novel investigation of patient care processes, improvements in new mobile healthcare technological developments aid real-time data collection, archiving and analysis of data in distributed environments.
Keywords: Healthcare, Big data, Activity Patterns Likelihood.
Scope of the Article: Healthcare Informatics